Intermediate-Task Training vs Direct Fine-Tuning for Non-English Reasoning in mT5 and XLM-R

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on non-English target tasks. Using nine intermediate language-understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on the XTREME benchmark. We see large improvements from intermediate training on the BUCC and Tatoeba sentence retrieval tas

Research goal: How does intermediate-task training on mT5 and XLM-R compare to direct fine-tuning in terms of reasoning capabilities in non-English languages, as measured by metrics like XTREME accuracy, and can intermediate tasks improve logical consistency across languages?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.7/10.